Simplex Sigillum Veri

Menu

Tag Archives: RDF

Last year, a company called DWave Systems announced their quantum computer (the ‘Orion’) – another milestone on the road to practical quantum computing. Their controversial claims seem worthy in their own right but they are particularly important to the semantic web (SW) community. The significance to the SW community was that their quantum computer solved problems akin to Grover’s Algorithm speeding up queries of disorderly databases.

Semantic web databases are not (completely) disorderly and there are many ways to optimize the search for matching triples to a graph pattern. What strikes me is that the larger the triple store, the more compelling the case for using some kind of quantum search algorithm to find matches. DWave are currently trialing 128qbit processors, and they claim their systems can scale, so I (as a layman) can see no reason why such computers couldn’t be used to help improve the performance of queries in massive triple stores.

What I wonder is:

what kind of indexing schemes can be used to impose structure on the triples in a store?

how can one adapt a B-tree to index each element of a triple rather than just a single primary key – three indexes seems extravagant.

are there quantum algorithms that can beat the best of these schemes?

is there is a place for quantum superposition in a graph matching algorithm (to simultaneously find matching triples then cancel out any that don’t match all the basic graph patterns?)

if DWave’s machines could solve NP-Complete problems, does that mean that we would then just use OWL-Full?

would the speed-ups then be great enough to consider linking everyday app data to large scale-upper ontologies?

is a contradiction in a ‘quantum reasoner’ (i.e. a reasoner that uses a quantum search engine) something that can never occur because it just cancels out and never appears in the returned triples? Would any returned conclusion be necessarily true (relative to the axioms of the ontology?)

Any thoughts?

UPDATEDWave are now working with Google to help them improve some of their machine learning algorithms. I wonder whether there will be other research into the practicality of using DWave quantum computing systems in conjunction with inference engines? This could, of course, open up whole new vistas of services that could be provided by Google (or their competitors). Either way, it gives me a warm feeling to know that every time I do a search, I’m getting the results from a quantum computer (no matter how indirectly). Nice.

They provide information on hosting RDF files as well as querying them using LinqToRdf. They show how easy it is to get semantic web applications up and running on .NET in no time at all. Please read the articles and share the links around.

John also told me about his new book LINQ for Dummies, which has a section on LinqToRdf. I’ve not had a chance to read it yet. I would welcome any feedback, which I’ll pass through to John. I understand that the content is broadly similar to the articles on DevSource.com, placing more emphasis on LINQ than RDF. Again, please take a look and let me know what you think.

LinqToRdf* is a full-featured LINQ** query provider for .NET written in C#. It provides developers with an intuitive way to make queries on semantic web databases. The project has been going for over a year and it’s starting to be noticed by semantic web early adopters and semantic web product vendors***. LINQ provides a standardised query language and a platform enabling any developer to understand systems using semantic web technologies via LinqToRdf. It will help those who don’t have the time to ascend the semantic web learning curve to become productive quickly.

The project’s progress and momentum needs to be sustained to help it become the standard API for semantic web development on the .NET platform. For that reason I’m appealing for volunteers to help with the development, testing, documentation and promotion of the project.

Please don’t be concerned that all the best parts of the project are done. Far from it! It’s more like the foundations are in place, and now the system can be used as a platform to add new features. There are many cool things that you could take on. Here are just a few:

Reverse engineering tool
This tool will use SPARQL to interrogate a remote store to get metadata to build an entity model.

Tutorials and Documentation
The documentation desperately needs the work of a skilled technical writer. I’ve worked hard to make LinqToRdf an easy tool to work with, but the semantic web is not a simple field. If it were, there’d be no need for LinqToRdf after all. This task will require an understanding of the LINQ, ASP.NET, C#, SPARQL, RDF, Turtle, and SemWeb.NET systems. It won’t be a walk in the park.

Supporting SQL Server
The SemWeb.NET API has recently added support to SQL Server, which has not been exploited inside LinqToRdf (although it may be easy to do).This task would also involve thinking about robust scalable architectures for semantic web applications in the .NET space.

Porting LinqToRdf to Mono
LINQ and C# 3.0 support in Mono is now mature enough to make this a desirable prospect. Nobody’s had the courage yet to tackle it. Clearly, this would massively extend the reach of LinqToRdf, and it would be helped by the fact that some of the underlying components are developed for Mono by default.

SPARQL Update (SPARUL) Support
LinqToRdf provides round-tripping only for locally stored RDF. Support of SPARQL Update would allow data round-tripping on remote stores. This is not a fully ratified standard, but it’s only a matter of time.

Demonstrators using large scale web endpoints
There are now quite a few large scale systems on the web with SPARQL endpoints. It would be a good demonstration of LinqToRdf to be able to mine them for useful data.

These are just some of the things that need to be done on the project. I’ve been hoping to tackle them all for some time, but there’s just too much for one man to do alone. If you have some time free and you want to learn more about LINQ or the Semantic Web, there is not a better project on the web for you to join.If you’re interested, reply to this letting me know how you could contribute, or what you want to tackle. Alternatively join the LinqToRdf discussion group and reply to this message there.

I’m very pleased to announce the release of version 0.8 of LinqToRdf. This release is significant for a couple of reasons. Firstly, because it provides a preview release of RdfMetal and secondly because it is the first release containing changes contributed by someone other than yours truly. The changes in this instance being provided by Carl Blakeley of OpenLink Software.

LinqToRdf v0.8 has received a few major chunks of work:

New installers for both the designer and the whole framework
WIX was proving to be a pain, so I downgraded to the integrated installer generator in Visual Studio.

A preview release of RdfMetal. I brought this release forward a little, on Carl Blakeley’s request, to coincide with a post he’s preparing on using OpenLink Virtuoso with LinqToRdf, so RdfMetal is not as fully baked as I’d planned. But it’s still worth a look. Expect a minor release in the next few weeks with additional fixes/enhancements.

I’d like to extend a very big thank-you to Carl for the the work he’s done in recent weeks to help extend and improve the mechanisms LinqToRdf uses to represent and traverse relationships. His contributions also include improvements in representing default graphs, and referencing multiple ontologies within a single .NET class. He also provided fixes around the quoting of URIs and some other fixes in the ways LinqToRdf generates SPARQL for default graphs. Carl also provided an interesting example application using OpenLink Virtuoso’s hosted version of Musicbrainz that is significantly richer than the test ontology I created for the unit tests and manuals.

I hope that Carl’s contributions represent an acknowledgement by OpenLink that not only does LinqToRdf support Virtuoso, but that there is precious little else in the .NET space that stands a chance of attracting developers to the semantic web. .NET is a huge untapped market for semantic web product vendors. LinqToRdf is, right now, the best way to get into semantic web development on .NET.

Look out for blog posts from Carl in the next day or two, about using LinqToRdf with OpenLink Virtuoso.